Integrated Sensing and Acting with Tunable Chemical Sensors

使用可调谐化学传感器集成传感和操作

• 批准号: 1002028
• 负责人: Ricardo Gutierrez-Osuna
• 金额: $ 29.99万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1002028&HistoricalAwards=false
• 关键词: Integrated; Sensing; Acting; Tunable; Chemical

AbstractResearch objectives and approaches. The objective of this research is to develop probabilistic methods for active sensing with tunable chemosensors. The approach is formulated as a partially observable Markov decision process, whereby the system takes sequential sensing actions to identify a chemical target. The investigators will develop algorithms to adapt sensor tunings on-the-fly for two types of scenario: target discrimination and background rejection. These algorithms will be validated on two tunable sensor technologies (metal-oxide chemoresistors and infrared interferometers) combined with tunable preconcentrators. Intellectual merit. Active sensing has received minimal attention in chemosensors, where most work has focused on off-line optimization. Likewise, chemometrics techniques assume that a full spectrum of the chemical is available. In contrast, the proposed work argues that only the most informative wavelengths/features are needed, and that these should be selected dynamically. This research will lead to agile systems that can adapt sensing parameters on-the-fly in response to their environments. The work will also provide a better understanding of active sensing across a range of scenarios (e.g. static vs. dynamic stimuli) and sensor characteristics (e.g. specific vs. cross-selective). Broader impacts. For a number of applications, from quality control to environmental monitoring, there is a need to rapidly detect, identify and quantify volatile compounds. The proposed methodology generalizes to a large class of chemical detectors, and may enable the development of power-aware networks of chemical sensors. The project will provide research opportunities for undergraduate students; since 2001, the PI has supervised 45 senior-design projects involving over 140 students.

Abstractresearch目标和方法。 这项研究的目的是开发使用可调化学传感器主动传感的概率方法。 该方法是部分可观察到的马尔可夫决策过程，因此系统采取顺序的传感动作来识别化学目标。 研究人员将开发算法以适应传感器调谐，以适应两种类型的方案：目标歧视和背景排斥。这些算法将在两种可调传感器技术（金属氧化物化学仪和红外干涉仪）与可调式预浓缩剂结合使用上进行验证。智力优点。主动传感在化学传感器中受到了最少的关注，在化学传感器中，大多数工作都集中在离线优化上。 同样，化学计量学技术假设可以提供全谱。 相比之下，拟议的工作认为只需要最有用的波长/特征，并且应动态选择这些功能。 这项研究将导致敏捷系统，该系统可以根据其环境而自然地调整传感参数。这项工作还将更好地理解各种场景（例如静态刺激与动态刺激）和传感器特征（例如特定与交叉选择性）的主动感测。 更广泛的影响。 对于许多应用，从质量控制到环境监测，都需要快速检测，识别和量化挥发性化合物。 拟议的方法将大量的化学探测器推广，并可以使能力感知的化学传感器网络的发展。 该项目将为本科生提供研究机会；自2001年以来，PI监督了45个高级设计项目，涉及140多名学生。